47 citations · 262 across the 38 of their papers we have counts for
11 papers · 1 filter
Algorithms for Adversarially Robust Deep Learning
Alexander Robey
Given the widespread use of deep learning models in safety-critical applications, ensuring that the decisions of such models are robust against adversarial exploitation is of funda…
Jailbreaking Black Box Large Language Models in Twenty Queries
Patrick Chao, Alexander Robey, Edgar Dobriban +3
There is growing interest in ensuring that large language models (LLMs) align with human values. However, the alignment of such models is vulnerable to adversarial jailbreaks, whic…
SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks
Alexander Robey, Eric Wong, Hamed Hassani +1
Despite efforts to align large language models (LLMs) with human intentions, widely-used LLMs such as GPT, Llama, and Claude are susceptible to jailbreaking attacks, wherein an adv…
Adversarial Training Should Be Cast as a Non-Zero-Sum Game
Alexander Robey, Fabian Latorre, George J. Pappas +2
One prominent approach toward resolving the adversarial vulnerability of deep neural networks is the two-player zero-sum paradigm of adversarial training, in which predictors are t…
Toward Certified Robustness Against Real-World Distribution Shifts
Haoze Wu, Teruhiro Tagomori, Alexander Robey +6
We consider the problem of certifying the robustness of deep neural networks against real-world distribution shifts. To do so, we bridge the gap between hand-crafted specifications…
Chordal Sparsity for Lipschitz Constant Estimation of Deep Neural Networks
Anton Xue, Lars Lindemann, Alexander Robey +3
Lipschitz constants of neural networks allow for guarantees of robustness in image classification, safety in controller design, and generalizability beyond the training data. As ca…
Probabilistically Robust Learning: Balancing Average- and Worst-case Performance
Alexander Robey, Luiz F. O. Chamon, George J. Pappas +1
Many of the successes of machine learning are based on minimizing an averaged loss function. However, it is well-known that this paradigm suffers from robustness issues that hinder…
On the Sample Complexity of Stability Constrained Imitation Learning
Stephen Tu, Alexander Robey, Tingnan Zhang +1
We study the following question in the context of imitation learning for continuous control: how are the underlying stability properties of an expert policy reflected in the sample…
Provable tradeoffs in adversarially robust classification
Edgar Dobriban, Hamed Hassani, David Hong +1
It is well known that machine learning methods can be vulnerable to adversarially-chosen perturbations of their inputs. Despite significant progress in the area, foundational open…
Model-Based Robust Deep Learning: Generalizing to Natural, Out-of-Distribution Data
Alexander Robey, Hamed Hassani, George J. Pappas
While deep learning has resulted in major breakthroughs in many application domains, the frameworks commonly used in deep learning remain fragile to artificially-crafted and imperc…
Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks
Mahyar Fazlyab, Alexander Robey, Hamed Hassani +2
Tight estimation of the Lipschitz constant for deep neural networks (DNNs) is useful in many applications ranging from robustness certification of classifiers to stability analysis…